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Petar Velichkovich

Published podcast speaker

Claims
4
Episodes
1
Shows
1
Named items
0

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What Petar said.

4 transcript-backed records

01 / recommendation

Just because we can achieve some level of moving the needle by hooking up a really potent tool to a language model doesn't mean that we shouldn't think about what would the next generation of these models look like and how can we make them intrinsically better.

“Just because we can achieve some level of moving the needle by hooking up a really potent tool to a language model doesn't mean that we shouldn't think about what would the next generation of these models look like and how can we make them intrinsically better.”
Publisher
Machine Learning Street Talk

02 / evaluation

We are working in this high dimensional space, which is not necessarily easily interpretable or composable because you have no easy way of saying, for example, in in theoretical computer science, if you want to compose 2 algorithms, you're working with them in a very abstract space, which means that, you know, you can easily reason about stitching the output of 1 to the input of another, whereas you cannot make that easy of a claim about latent spaces of 2 neural networks.

“We are working in this high dimensional space, which is not necessarily easily interpretable or composable because you have no easy way of saying, for example, in in theoretical computer science, if you want to compose 2 algorithms, you're working with them in a very abstract space, which means that, you know, you can easily reason about stitching the output of 1 to the input of another, whereas you cannot make that easy of a claim about latent spaces of 2 neural networks.”
Publisher
Machine Learning Street Talk

03 / evaluation

When you think about all of the big scientific advances that were done with large language models, for example, up to this date, I would argue most of the ones I'm personally familiar with are a result of a careful combination of a large language model and an algorithmic procedure in the background, which actually makes sure to give it robustness properties.

“When you think about all of the big scientific advances that were done with large language models, for example, up to this date, I would argue most of the ones I'm personally familiar with are a result of a careful combination of a large language model and an algorithmic procedure in the background, which actually makes sure to give it robustness properties.”
Publisher
Machine Learning Street Talk

04 / prediction

As you can see, if you ask me to multiply 2 numbers that are, like, 50 digits long, I will definitely make some failures if you ask me to do that. But, you know, the point is that, like, what I would like is that, a system understands the amount of effort that needs to go into doing some kind of computation and maybe at least to give me some either an estimate of how likely it is to make mistakes or some notion even some notion of, I'm sorry.

“As you can see, if you ask me to multiply 2 numbers that are, like, 50 digits long, I will definitely make some failures if you ask me to do that. But, you know, the point is that, like, what I would like is that, a system understands the amount of effort that needs to go into doing some kind of computation and maybe at least to give me some either an estimate of how likely it is to make mistakes or some notion even some notion of, I'm sorry.”
Publisher
Machine Learning Street Talk
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